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Precision and Recall — Machine Learning Zoomcamp Module 4

Precision tell us the fraction of positive predictions that are correct. It takes into account only the positive class (TP and FP - second column of the confusion matrix), as is stated in the following formula:

Machine Learning Zoomcamp › Module 4: Evaluation Metrics for Classification › Precision and Recall

Notes

Precision tell us the fraction of positive predictions that are correct. It takes into account only the positive class (TP and FP - second column of the confusion matrix), as is stated in the following formula:

Recall measures the fraction of correctly identified positive instances. It considers parts of the positive and negative classes (TP and FN - second row of confusion table). The formula of this metric is presented below:

In this problem, the precision and recall values were 67% and 54% respectively. So, these measures reflect some errors of our model that accuracy did not notice due to the class imbalance.

!classification_metrics.png

MNEMONICS:

• Precision : From the predicted positives, how many we predicted right. See how the word precision is similar to the word prediction?

• Recall : From the real positives, how many we predicted right. See how the word recall is similar to the word real?

Add notes from the video (PRs are welcome)

• Notes from Peter Ernicke

Key concepts

• Classification Metrics

Related notes

• mlz-m02-car-price-prediction-project
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-feature-engineering
• mlz-m02-root-mean-squared-error-rmse
• mlz-m02-using-the-model
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-confusion-table
• mlz-m04-cross-validation
• mlz-m04-evaluation-metrics-session-overview
• mlz-m04-explore-more
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m04-summary
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-selecting-the-best-model
• mlz-m06-xgboost-parameter-tuning

Sources

• Video
• Lesson file